Domain Adaptation for Sentiment Analysis using Keywords in the Target Domain as the Learning Weight

摘要

This paper proposes a new method of instance-based domain adaptation for sentiment analysis. First, our method defines the likelihood of keywords, through the value of inverse document frequency (IDF), for each word in documents in the target domain. Next, the keyword content rate of a document is calculated using the likelihood of keywords and the domain adaptation is performed by giving the keyword content rate to each document in the source domain as the weight. The experiment used an Amazon dataset to demonstrate the effectiveness of our proposed method. Although the instance-based method has not shown great efficiency, the advantages combining instance-based method and feature-based method are shown in this paper.